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dc.contributor.authorCuevas, E.
dc.contributor.authorZaldivar, D.
dc.contributor.authorRojas, R.
dc.description.abstractReal-time gaze control is a complicated task because of the different dynamics of the elements involved in the process. On the one hand, the algorithms for image processing are usually very time-consuming. On the other hand, the motors and mechanisms used to control camera movements are very slow. This work describes the use of an adaptive network-based fuzzy inference system (ANFIS) model to reduce the delay effects in gaze control and also explains how the delay problem is resolved through prediction of the target movement using a neurofuzzy approach. The approach has been successfully tested in the vision system of a humanoid robot. The predictions improve the velocity and accuracy of object tracking. © 2005 IEEE.
dc.titleNeurofuzzy prediction for gaze control
dc.relation.ispartofjournalCanadian Journal of Electrical and Computer Engineering
dc.subject.keywordGaze control; Neurofuzzy systems; Prediction systems
dc.contributor.affiliationCuevas, E., University of Guadalajara, Guadalajara, Mexico; Zaldivar, D., University of Guadalajara, Guadalajara, Mexico, Freie Universit at Berlin, Berlin, Germany; Rojas, R., Jesuit University of Guadalajara (ITESO), Guadalajara, Mexico, National Polytechnic Institute (IPN), Mexico City, Mexico
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